




'''simple docstring'''



import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
      import torch


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


UpperCamelCase_    	=				logging.get_logger(__name__)
UpperCamelCase_    	=				{"vocab_file": "spiece.model"}

UpperCamelCase_    	=				{
    "vocab_file": {
        "AI-Sweden/gpt-sw3-126m": "https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-350m": "https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-1.6b": "https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-6.7b": "https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model",
        "AI-Sweden/gpt-sw3-20b": "https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model",
    }
}

UpperCamelCase_    	=				{
    "AI-Sweden/gpt-sw3-126m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-350m": 2_0_4_8,
    "AI-Sweden/gpt-sw3-1.6b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-6.7b": 2_0_4_8,
    "AI-Sweden/gpt-sw3-20b": 2_0_4_8,
}






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       A						:				int        =						VOCAB_FILES_NAMES
       A						:				Union[str, Any]        =						PRETRAINED_VOCAB_FILES_MAP
       A						:				Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       A						:				Tuple        =						['''input_ids''', '''attention_mask''']







       def __init__(      self,							A,							A=False,							A=False,							A=False,							A=None,							A=None,							A=None,							A=None,							A = None,							**A,							):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[Any]    					=						{} if sp_model_kwargs is None else sp_model_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						kwargs.get('name_or_path'  )
             if name_or_path is None:
                   logger.warning(
                       'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
                       ' you are testing the model, this can safely be ignored'  )
                   SCREAMING_SNAKE_CASE   :      Dict    					=						'None'

             # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						'<|endoftext|>' if eos_token is None else eos_token
             SCREAMING_SNAKE_CASE   :      int    					=						'<unk>' if unk_token is None else unk_token
             if "gpt-sw3-7b" in name_or_path:
                   SCREAMING_SNAKE_CASE   :      Dict    					=						unk_token if pad_token is None else pad_token
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						eos_token if bos_token is None else bos_token
             else:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						'<pad>' if pad_token is None else pad_token
                   SCREAMING_SNAKE_CASE   :      int    					=						'<s>' if bos_token is None else bos_token

             super().__init__(
                 do_lower_case=A,							remove_space=A,							keep_accents=A,							bos_token=A,							eos_token=A,							unk_token=A,							pad_token=A,							sp_model_kwargs=self.sp_model_kwargs,							**A,							)

             SCREAMING_SNAKE_CASE   :      Tuple    					=						do_lower_case
             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						remove_space
             SCREAMING_SNAKE_CASE   :      List[Any]    					=						keep_accents
             SCREAMING_SNAKE_CASE   :      Tuple    					=						vocab_file

             SCREAMING_SNAKE_CASE   :      Dict    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.Load(A  )

             # Used for whitespace normalization in input texts
             # fmt : off
             SCREAMING_SNAKE_CASE   :      Any    					=						{' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
             # fmt : on

             # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
             SCREAMING_SNAKE_CASE   :      str    					=						re.compile(
                 F"[{''.join(map(A,							list(range(0,							9  )  ) + list(range(11,							32  )  ) + list(range(127,							160  )  ) + [160, 173, 8_203]  )  )}]"  )







       def __getstate__(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						self.__dict__.copy()
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						None
             return state







       def __setstate__(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Dict    					=						d

             # for backward compatibility
             if not hasattr(self,							'sp_model_kwargs'  ):
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}

             SCREAMING_SNAKE_CASE   :      List[Any]    					=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
             self.sp_model.Load(self.vocab_file  )







       @property
       # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             return len(self.sp_model  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.non_printing_characters_re.sub('',							A  )

             # Normalize whitespaces
             SCREAMING_SNAKE_CASE   :      Any    					=						''.join([char if char not in self.whitespaces else ' ' for char in text]  )

             # NFC Unicode normalization
             SCREAMING_SNAKE_CASE   :      List[str]    					=						unicodedata.normalize('NFC',							A  )
             return text







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      List[str]    					=						self.preprocess_text(A  )
             return self.sp_model.encode(A,							out_type=A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.PieceToId(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.IdToPiece(A  )







       @staticmethod
       def        UpperCamelCase_							(      A  ):







             '''simple docstring'''







             return out_string







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						[]
             SCREAMING_SNAKE_CASE   :      str    					=						''
             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						False
             for token in tokens:
                   # make sure that special tokens are not decoded using sentencepiece model
                   if token in self.all_special_tokens:
                         # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                         if not prev_is_special:
                               out_string += " "

                         out_string += self.sp_model.decode(A  ) + token
                         SCREAMING_SNAKE_CASE   :      str    					=						True
                         SCREAMING_SNAKE_CASE   :      List[str]    					=						[]
                   else:
                         current_sub_tokens.append(A  )
                         SCREAMING_SNAKE_CASE   :      int    					=						False
             out_string += self.sp_model.decode(A  )

             return out_string







       def        UpperCamelCase_							(      self  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Optional[int]    					=						{self.convert_ids_to_tokens(A  ): i for i in range(self.vocab_size  )}
             vocab.update(self.added_tokens_encoder  )
             return vocab







       def        UpperCamelCase_							(      self,							A,							A = None  ):







             '''simple docstring'''







             if not os.path.isdir(A  ):
                   logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
                   return
             SCREAMING_SNAKE_CASE   :      Any    					=						os.path.join(
                 A,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

             if os.path.abspath(self.vocab_file  ) != os.path.abspath(A  ) and os.path.isfile(self.vocab_file  ):
                   copyfile(self.vocab_file,							A  )
             elif not os.path.isfile(self.vocab_file  ):
                   with open(A,							'wb'  ) as fi:
                         SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						self.sp_model.serialized_model_proto()
                         fi.write(A  )

             return (out_vocab_file,)







       def        UpperCamelCase_							(      self,							A,							A = False  ):







             '''simple docstring'''







             if isinstance(A,							A  ):
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						self.preprocess_text(A  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						self.sp_model.encode(A  )
             else:
                   SCREAMING_SNAKE_CASE   :      List[Any]    					=						[self.preprocess_text(A  ) for t in text]
                   SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						self.sp_model.encode(A  )

             if return_tensors is True or return_tensors == "pt":
                   SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						torch.tensor(A  )

             return token_ids







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             return self.sp_model.decode(A  )







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      str    					=						[F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
             SCREAMING_SNAKE_CASE   :      Any    					=						(
                 F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(A  ) + F"{self.bos_token}Bot:"
             )
             return self.encode(text=A  )




"""simple docstring"""



import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    _UpperCAmelCase        = SwinaSRConfig()

    if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = 4
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = 4_8
        _UpperCAmelCase        = """pixelshuffle_aux"""
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = 6_0
        _UpperCAmelCase        = [6, 6, 6, 6]
        _UpperCAmelCase        = """pixelshuffledirect"""
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = 4
        _UpperCAmelCase        = """nearest+conv"""
    elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = 1_2_6
        _UpperCAmelCase        = 7
        _UpperCAmelCase        = 255.0
        _UpperCAmelCase        = """"""

    return config


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Tuple:





    """simple docstring"""

    if "patch_embed.proj" in name and "layers" not in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """embeddings.patch_embeddings.projection"""   )
    if "patch_embed.norm" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.norm"""       ,   """embeddings.patch_embeddings.layernorm"""   )
    if "layers" in name:
        _UpperCAmelCase        = name.replace("""layers"""       ,   """encoder.stages"""   )
    if "residual_group.blocks" in name:
        _UpperCAmelCase        = name.replace("""residual_group.blocks"""       ,   """layers"""   )
    if "attn.proj" in name:
        _UpperCAmelCase        = name.replace("""attn.proj"""       ,   """attention.output.dense"""   )
    if "attn" in name:
        _UpperCAmelCase        = name.replace("""attn"""       ,   """attention.self"""   )
    if "norm1" in name:
        _UpperCAmelCase        = name.replace("""norm1"""       ,   """layernorm_before"""   )
    if "norm2" in name:
        _UpperCAmelCase        = name.replace("""norm2"""       ,   """layernorm_after"""   )
    if "mlp.fc1" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc1"""       ,   """intermediate.dense"""   )
    if "mlp.fc2" in name:
        _UpperCAmelCase        = name.replace("""mlp.fc2"""       ,   """output.dense"""   )
    if "q_bias" in name:
        _UpperCAmelCase        = name.replace("""q_bias"""       ,   """query.bias"""   )
    if "k_bias" in name:
        _UpperCAmelCase        = name.replace("""k_bias"""       ,   """key.bias"""   )
    if "v_bias" in name:
        _UpperCAmelCase        = name.replace("""v_bias"""       ,   """value.bias"""   )
    if "cpb_mlp" in name:
        _UpperCAmelCase        = name.replace("""cpb_mlp"""       ,   """continuous_position_bias_mlp"""   )
    if "patch_embed.proj" in name:
        _UpperCAmelCase        = name.replace("""patch_embed.proj"""       ,   """patch_embed.projection"""   )

    if name == "norm.weight":
        _UpperCAmelCase        = """layernorm.weight"""
    if name == "norm.bias":
        _UpperCAmelCase        = """layernorm.bias"""

    if "conv_first" in name:
        _UpperCAmelCase        = name.replace("""conv_first"""       ,   """first_convolution"""   )

    if (
        "upsample" in name
        or "conv_before_upsample" in name
        or "conv_bicubic" in name
        or "conv_up" in name
        or "conv_hr" in name
        or "conv_last" in name
        or "aux" in name
    ):
        # heads
        if "conv_last" in name:
            _UpperCAmelCase        = name.replace("""conv_last"""       ,   """final_convolution"""   )
        if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
            if "conv_before_upsample.0" in name:
                _UpperCAmelCase        = name.replace("""conv_before_upsample.0"""       ,   """conv_before_upsample"""   )
            if "upsample.0" in name:
                _UpperCAmelCase        = name.replace("""upsample.0"""       ,   """upsample.convolution_0"""   )
            if "upsample.2" in name:
                _UpperCAmelCase        = name.replace("""upsample.2"""       ,   """upsample.convolution_1"""   )
            _UpperCAmelCase        = """upsample.""" + name
        elif config.upsampler == "pixelshuffledirect":
            _UpperCAmelCase        = name.replace("""upsample.0.weight"""       ,   """upsample.conv.weight"""   )
            _UpperCAmelCase        = name.replace("""upsample.0.bias"""       ,   """upsample.conv.bias"""   )
        else:
            pass
    else:
        _UpperCAmelCase        = """swin2sr.""" + name

    return name


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	int:





    """simple docstring"""

    for key in orig_state_dict.copy().keys():
        _UpperCAmelCase        = orig_state_dict.pop(_A   )

        if "qkv" in key:
            _UpperCAmelCase        = key.split("""."""   )
            _UpperCAmelCase        = int(key_split[1]   )
            _UpperCAmelCase        = int(key_split[4]   )
            _UpperCAmelCase        = config.embed_dim

            if "weight" in key:
                _UpperCAmelCase        = val[:dim, :]
                _UpperCAmelCase        = val[dim : dim * 2, :]
                _UpperCAmelCase        = val[-dim:, :]
            else:
                _UpperCAmelCase        = val[:dim]
                _UpperCAmelCase        = val[dim : dim * 2]
                _UpperCAmelCase        = val[-dim:]
            pass
        else:
            _UpperCAmelCase        = val

    return orig_state_dict


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A   )     ->	List[Any]:





    """simple docstring"""

    _UpperCAmelCase        = get_config(_A   )
    _UpperCAmelCase        = SwinaSRForImageSuperResolution(_A   )
    model.eval()

    _UpperCAmelCase        = torch.hub.load_state_dict_from_url(_A       ,   map_location="""cpu"""   )
    _UpperCAmelCase        = convert_state_dict(_A       ,   _A   )
    _UpperCAmelCase     ,_UpperCAmelCase        = model.load_state_dict(_A       ,   strict=_A   )

    if len(_A   ) > 0:
        raise ValueError("""Missing keys when converting: {}""".format(_A   )   )
    for key in unexpected_keys:
        if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
            raise ValueError(F"""Unexpected key {key} in state_dict"""   )

    # verify values
    _UpperCAmelCase        = """https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"""
    _UpperCAmelCase        = Image.open(requests.get(_A       ,   stream=_A   ).raw   ).convert("""RGB"""   )
    _UpperCAmelCase        = SwinaSRImageProcessor()
    # pixel_values = processor(image, return_tensors="pt").pixel_values

    _UpperCAmelCase        = 1_2_6 if """Jpeg""" in checkpoint_url else 2_5_6
    _UpperCAmelCase        = Compose(
        [
            Resize((image_size, image_size)   ),
            ToTensor(),
            Normalize(mean=[0.485, 0.456, 0.406]       ,   std=[0.229, 0.224, 0.225]   ),
        ]   )
    _UpperCAmelCase        = transforms(_A   ).unsqueeze(0   )

    if config.num_channels == 1:
        _UpperCAmelCase        = pixel_values[:, 0, :, :].unsqueeze(1   )

    _UpperCAmelCase        = model(_A   )

    # assert values
    if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_087, -0.7_138, -0.6_721], [-0.8_340, -0.8_095, -0.7_298], [-0.9_149, -0.8_414, -0.7_940]]   )
    elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_775, -0.8_105, -0.8_933], [-0.7_764, -0.8_356, -0.9_225], [-0.7_976, -0.8_686, -0.9_579]]   )
    elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
        # TODO values didn't match exactly here
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.8_035, -0.7_504, -0.7_491], [-0.8_538, -0.8_124, -0.7_782], [-0.8_804, -0.8_651, -0.8_493]]   )
    elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 5_1_2, 5_1_2]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.7_669, -0.8_662, -0.8_767], [-0.8_810, -0.9_962, -0.9_820], [-0.9_340, -1.0_322, -1.1_149]]   )
    elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
        _UpperCAmelCase        = torch.Size([1, 3, 1_0_2_4, 1_0_2_4]   )
        _UpperCAmelCase        = torch.tensor(
            [[-0.5_238, -0.5_557, -0.6_321], [-0.6_016, -0.5_903, -0.6_391], [-0.6_244, -0.6_334, -0.6_889]]   )

    assert (
        outputs.reconstruction.shape == expected_shape
    ), F"""Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}"""
    assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]       ,   _A       ,   atol=1e-3   )
    print("""Looks ok!"""   )

    _UpperCAmelCase        = {
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth""": (
            """swin2SR-classical-sr-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth""": (
            """swin2SR-classical-sr-x4-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth""": (
            """swin2SR-compressed-sr-x4-48"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth""": (
            """swin2SR-lightweight-x2-64"""
        ),
        """https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth""": (
            """swin2SR-realworld-sr-x4-64-bsrgan-psnr"""
        ),
    }
    _UpperCAmelCase        = url_to_name[checkpoint_url]

    if pytorch_dump_folder_path is not None:
        print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
        model.save_pretrained(_A   )
        print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
        processor.save_pretrained(_A   )

    if push_to_hub:
        model.push_to_hub(F"""caidas/{model_name}"""   )
        processor.push_to_hub(F"""caidas/{model_name}"""   )


if __name__ == "__main__":
 a       :						Optional[int]       		=		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--checkpoint_url''',
     default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
     type=str,
     help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

 a       :						str       		=		parser.parse_args()
 convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)